example of validity in research

Frequently Asked Questions

What is reliability and validity in research.

Reliability in research refers to the consistency and stability of measurements or findings. Validity relates to the accuracy and truthfulness of results, measuring what the study intends to. Both are crucial for trustworthy and credible research outcomes.

What is validity?

Validity in research refers to the extent to which a study accurately measures what it intends to measure. It ensures that the results are truly representative of the phenomena under investigation. Without validity, research findings may be irrelevant, misleading, or incorrect, limiting their applicability and credibility.

What is reliability?

Reliability in research refers to the consistency and stability of measurements over time. If a study is reliable, repeating the experiment or test under the same conditions should produce similar results. Without reliability, findings become unpredictable and lack dependability, potentially undermining the study’s credibility and generalisability.

What is reliability in psychology?

In psychology, reliability refers to the consistency of a measurement tool or test. A reliable psychological assessment produces stable and consistent results across different times, situations, or raters. It ensures that an instrument’s scores are not due to random error, making the findings dependable and reproducible in similar conditions.

What is test retest reliability?

Test-retest reliability assesses the consistency of measurements taken by a test over time. It involves administering the same test to the same participants at two different points in time and comparing the results. A high correlation between the scores indicates that the test produces stable and consistent results over time.

How to improve reliability of an experiment?

• Standardise procedures and instructions.
• Use consistent and precise measurement tools.
• Train observers or raters to reduce subjective judgments.
• Increase sample size to reduce random errors.
• Conduct pilot studies to refine methods.
• Repeat measurements or use multiple methods.
• Address potential sources of variability.

What is the difference between reliability and validity?

Reliability refers to the consistency and repeatability of measurements, ensuring results are stable over time. Validity indicates how well an instrument measures what it’s intended to measure, ensuring accuracy and relevance. While a test can be reliable without being valid, a valid test must inherently be reliable. Both are essential for credible research.

Are interviews reliable and valid?

Interviews can be both reliable and valid, but they are susceptible to biases. The reliability and validity depend on the design, structure, and execution of the interview. Structured interviews with standardised questions improve reliability. Validity is enhanced when questions accurately capture the intended construct and when interviewer biases are minimised.

Are IQ tests valid and reliable?

IQ tests are generally considered reliable, producing consistent scores over time. Their validity, however, is a subject of debate. While they effectively measure certain cognitive skills, whether they capture the entirety of “intelligence” or predict success in all life areas is contested. Cultural bias and over-reliance on tests are also concerns.

Are questionnaires reliable and valid?

Questionnaires can be both reliable and valid if well-designed. Reliability is achieved when they produce consistent results over time or across similar populations. Validity is ensured when questions accurately measure the intended construct. However, factors like poorly phrased questions, respondent bias, and lack of standardisation can compromise their reliability and validity.

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Quantitative research is associated with measurable numerical data. Qualitative research is where a researcher collects evidence to seek answers to a question.

A survey includes questions relevant to the research topic. The participants are selected, and the questionnaire is distributed to collect the data.

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The 4 Types of Validity in Research Design (+3 More to Consider)

The conclusions you draw from your research (whether from analyzing surveys, focus groups, experimental design, or other research methods) are only useful if they’re valid.

How “true” are these results? How well do they represent the thing you’re actually trying to study? Validity is used to determine whether research measures what it intended to measure and to approximate the truthfulness of the results.

Unfortunately, researchers sometimes create their own definitions when it comes to what is considered valid.

• In quantitative research testing for validity and reliability is a given.
• However, some qualitative researchers have gone so far as to suggest that validity does not apply to their research even as they acknowledge the need for some qualifying checks or measures in their work.

This is wrong. Validity is always important – even if it’s harder to determine in qualitative research.

To disregard validity is to put the trustworthiness of your work in question and to call into question others’ confidence in its results. Even when qualitative measures are used in research, they need to be looked at using measures of reliability and validity in order to sustain the trustworthiness of the results.

What is validity in research?

Validity is how researchers talk about the extent to which results represent reality. Research methods, quantitative or qualitative, are methods of studying real phenomenon – validity refers to how much of that phenomenon they measure vs. how much “noise,” or unrelated information, is captured by the results.

Validity and reliability make the difference between “good” and “bad” research reports. Quality research depends on a commitment to testing and increasing the validity as well as the reliability of your research results.

Any research worth its weight is concerned with whether what is being measured is what is intended to be measured and considers how observations are influenced by the circumstances in which they are made.

The basis of how our conclusions are made plays an important role in addressing the broader substantive issues of any given study.

For this reason, we are going to look at various validity types that have been formulated as a part of legitimate research methodology.

Here are the 7 key types of validity in research:

• Face validity
• Content validity
• Construct validity
• Internal validity
• External validity
• Statistical conclusion validity
• Criterion-related validity

1. Face validity

Face validity is how valid your results seem based on what they look like. This is the least scientific method of validity, as it is not quantified using statistical methods.

Face validity is not validity in a technical sense of the term.  It is concerned with whether it seems like we measure what we claim.

Here we look at how valid a measure appears on the surface and make subjective judgments based on that.

For example,

• Imagine you give a survey that appears to be valid to the respondent and the questions are selected because they look valid to the administer.
• The administer asks a group of random people, untrained observers if the questions appear valid to them

In research, it’s never enough to rely on face judgments alone – and more quantifiable methods of validity are necessary to draw acceptable conclusions.  There are many instruments of measurement to consider so face validity is useful in cases where you need to distinguish one approach over another.

Face validity should never be trusted on its own merits.

2. Content validity

Content validity is whether or not the measure used in the research covers all of the content in the underlying construct (the thing you are trying to measure).

This is also a subjective measure, but unlike face validity, we ask whether the content of a measure covers the full domain of the content. If a researcher wanted to measure introversion, they would have to first decide what constitutes a relevant domain of content for that trait.

Content validity is considered a subjective form of measurement because it still relies on people’s perceptions for measuring constructs that would otherwise be difficult to measure.

Where content validity distinguishes itself (and becomes useful) through its use of experts in the field or individuals belonging to a target population. This study can be made more objective through the use of rigorous statistical tests.

For example, you could have a content validity study that informs researchers how items used in a survey represent their content domain, how clear they are, and the extent to which they maintain the theoretical factor structure assessed by the factor analysis.

3. Construct validity

A construct represents a collection of behaviors that are associated in a meaningful way to create an image or an idea invented for a research purpose. Construct validity is the degree to which your research measures the construct (as compared to things outside the construct).

Depression is a construct that represents a personality trait that manifests itself in behaviors such as oversleeping, loss of appetite, difficulty concentrating, etc.

The existence of a construct is manifest by observing the collection of related indicators.  Any one sign may be associated with several constructs.  A person with difficulty concentrating may have ADHD but not depression.

Construct validity is the degree to which inferences can be made from operationalizations (connecting concepts to observations) in your study to the constructs on which those operationalizations are based.  To establish construct validity you must first provide evidence that your data supports the theoretical structure.

You must also show that you control the operationalization of the construct, in other words, show that your theory has some correspondence with reality.

• Convergent Validity –  the degree to which an operation is similar to other operations it should theoretically be similar to.
• Discriminative Validity -– if a scale adequately differentiates itself or does not differentiate between groups that should differ or not differ based on theoretical reasons or previous research.
• Nomological Network –  representation of the constructs of interest in a study, their observable manifestations, and the interrelationships among and between these.  According to Cronbach and Meehl,  a nomological network has to be developed for a measure for it to have construct validity
• Multitrait-Multimethod Matrix –  six major considerations when examining Construct Validity according to Campbell and Fiske.  This includes evaluations of convergent validity and discriminative validity.  The others are trait method unit, multi-method/trait, truly different methodology, and trait characteristics.

4. Internal validity

Internal validity refers to the extent to which the independent variable can accurately be stated to produce the observed effect.

If the effect of the dependent variable is only due to the independent variable(s) then internal validity is achieved. This is the degree to which a result can be manipulated.

Put another way, internal validity is how you can tell that your research “works” in a research setting. Within a given study, does the variable you change affect the variable you’re studying?

5. External validity

External validity refers to the extent to which the results of a study can be generalized beyond the sample. Which is to say that you can apply your findings to other people and settings.

Think of this as the degree to which a result can be generalized. How well do the research results apply to the rest of the world?

A laboratory setting (or other research setting) is a controlled environment with fewer variables. External validity refers to how well the results hold, even in the presence of all those other variables.

6. Statistical conclusion validity

Statistical conclusion validity is a determination of whether a relationship or co-variation exists between cause and effect variables.

This type of validity requires:

• Ensuring adequate sampling procedures
• Appropriate statistical tests
• Reliable measurement procedures

This is the degree to which a conclusion is credible or believable.

7. Criterion-related validity

Criterion-related validity (also called instrumental validity) is a measure of the quality of your measurement methods.  The accuracy of a measure is demonstrated by comparing it with a measure that is already known to be valid.

In other words – if your measure has a high correlation with other measures that are known to be valid because of previous research.

For this to work you must know that the criterion has been measured well.  And be aware that appropriate criteria do not always exist.

What you are doing is checking the performance of your operationalization against criteria.

The criteria you use as a standard of judgment accounts for the different approaches you would use:

• Predictive Validity –  operationalization’s ability to predict what it is theoretically able to predict.  The extent to which a measure predicts expected outcomes.
• Concurrent Validity –  operationalization’s ability to distinguish between groups it theoretically should be able to.  This is where a test correlates well with a measure that has been previously validated.

When we look at validity in survey data we are asking whether the data represents what we think it should represent.

We depend on the respondent’s mindset and attitude to give us valid data.

In other words, we depend on them to answer all questions honestly and conscientiously. We also depend on whether they are able to answer the questions that we ask. When questions are asked that the respondent can not comprehend or understand, then the data does not tell us what we think it does.

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9 Types of Validity in Research

Dave Cornell (PhD)

Dr. Cornell has worked in education for more than 20 years. His work has involved designing teacher certification for Trinity College in London and in-service training for state governments in the United States. He has trained kindergarten teachers in 8 countries and helped businessmen and women open baby centers and kindergartens in 3 countries.

Learn about our Editorial Process

Chris Drew (PhD)

This article was peer-reviewed and edited by Chris Drew (PhD). The review process on Helpful Professor involves having a PhD level expert fact check, edit, and contribute to articles. Reviewers ensure all content reflects expert academic consensus and is backed up with reference to academic studies. Dr. Drew has published over 20 academic articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education and holds a PhD in Education from ACU.

Validity refers to whether or not a test or an experiment is actually doing what it is intended to do.

Validity sits upon a spectrum. For example:

• Low Validity: Most people now know that the standard IQ test does not actually measure intelligence or predict success in life.
• High Validity: By contrast, a standard pregnancy test is about 99% accurate , meaning it has very high validity and is therefore a very reliable test.

There are many ways to determine validity. Most of them are defined below.

Types of Validity

1. face validity.

Face validity refers to whether a scale “appears” to measure what it is supposed to measure. That is, do the questions seem to be logically related to the construct under study.

For example, a personality scale that measures emotional intelligence should have questions about self-awareness and empathy. It should not have questions about math or chemistry.

One common way to assess face validity is to ask a panel of experts to examine the scale and rate it’s appropriateness as a tool for measuring the construct. If the experts agree that the scale measures what it has been designed to measure, then the scale is said to have face validity.

If a scale, or a test, doesn’t have face validity, then people taking it won’t be serious.

Conbach explains it in the following way:

“When a patient loses faith in the medicine his doctor prescribes, it loses much of its power to improve his health. He may skip doses, and in the end may decide doctors cannot help him and let treatment lapse all together. For similar reasons, when selecting a test one must consider how worthwhile it will appear to the participant who takes it and other laymen who will see the results” (Cronbach, 1970, p. 182).

2. Content Validity

Content validity refers to whether a test or scale is measuring all of the components of a given construct. For example, if there are five dimensions of emotional intelligence (EQ), then a scale that measures EQ should contain questions regarding each dimension.

Similar to face validity, content validity can be assessed by asking subject matter experts (SMEs) to examine the test. If experts agree that the test includes items that assess every domain of the construct, then the test has content validity.

For example, the math portion of the SAT contains questions that require skills in many types of math: arithmetic, algebra, geometry, calculus, and many others. Since there are questions that assess each type of math, then the test has content validity.

The developer of the test could ask SMEs to rate the test’s construct validity. If the SMEs all give the test high ratings, then it has construct validity.

3. Construct Validity

Construct validity is the extent to which a measurement tool is truly assessing what it has been designed to assess.

There are two main methods of assessing construct validity: convergent and discriminant validity.

Convergent validity involves taking two tests that are supposed to measure the same construct and administering them to a sample of participants. The higher the correlation between the two tests, the stronger the construct validity.

With divergent validity, two tests that measure completely different constructs are administered to the same sample of participants. Since the tests are measuring different constructs, there should be a very low correlation between the two.

4. Internal Validity

Internal validity refers to whether or not the results of an experiment are due to the manipulation of the independent, or treatment, variables. For example, a researcher wants to examine how temperature affects willingness to help, so they have research participants wait in a room.

There are different rooms, one has the temperature set at normal, one at moderately warm, and the other at very warm.

During the next phase of the study, participants are asked to donate to a local charity before taking part in the rest of the study. The results showed that as the temperature of the room increased, donations decreased.

On the surface, it seems as though the study has internal validity: room temperature affected donations. However, even though the experiment involved three different rooms set at different temperatures, each room was a different size. The smallest room was the warmest and the normal temperature room was the largest.

Now, we don’t know if the donations were affected by room temperature or room size. So, the study has questionable internal validity.

Another way internal validity is assessed is through inter-rater reliability measures, which helps bolster both the validity and reliability of the study.

5. External Validity

External validity refers to whether the results of a study generalize to the real world or other situations. A lot of psychological studies take place in a university lab. Therefore, the setting is not very realistic.

This creates a big problem regarding external validity. Can we say that what happens in a lab would be the same thing that would happen in the real world?

For example, a study on mindfulness involves the researcher randomly assigning different research participants to use one of three mindfulness apps on their phones at home every night for 3 weeks. At the end of three weeks, their level of stress is measured with some high-tech EEG equipment.

This study has external validity because the participants used real apps and they were at home when using those apps. The apps and the home setting are realistic, so the study has external validity. 

See More: Examples of External Validity

6. Concurrent Validity

Concurrent validity is a method of assessing validity that involves comparing a new test with an already existing test, or an already established criterion.

For example, a newly developed math test for the SAT will need to be validated before giving it to thousands of students. So, the new version of the test is administered to a sample of college math majors along with the old version of the test.

Scores on the two tests are compared by calculating a correlation between the two. The higher the correlation, the stronger the concurrent validity of the new test.

7. Predictive Validity

Predictive validity refers to whether scores on one test are associated with performance on a given criterion. That is, can a person’s score on the test predict their performance on the criterion?

For example, an IT company needs to hire dozens of programmers for an upcoming project. But conducting interviews with hundreds of applicants is time-consuming and not very accurate at identifying skilled coders.

So, the company develops a test that contains programming problems similar to the demands of the new project. The company assesses predictive validity of the test by having their current programmers take the test and then compare their scores with their yearly performance evaluations.

The results indicate that programmers with high marks in their evaluations also did very well on the test. Therefore, the test has predictive validity.  

Now, when new applicants’ take the test, the company can predict how well they will do at the job in the future. People that do well on the predictor variable test will most likely do well at the job.

8. Statistical Conclusion Validity

Statistical conclusion validity refers to whether the conclusions drawn by the authors of a study are supported by the statistical procedures.

For example, did the study apply the correct statistical analyses, were adequate sampling procedures implemented, did the study use measurement tools that are valid and reliable?

If the answers to those questions are all “yes,” then the study has statistical conclusion validity. However, if the some or all of the answers are “no,” then the conclusions of the study are called into question.

Using the wrong statistical analyses or basing the conclusions on very small sample sizes, make the results questionable. If the results are based on faulty procedures, then the conclusions cannot be accepted as valid.

9. Criterion Validity

Criterion validity is sometimes called predictive validity. It refers to how well scores on one measurement device are associated with scores on a given performance domain (the criterion).

For example, how well do SAT scores predict college GPA? Or, to what extent are measures of consumer confidence related to the economy?

An example of low criterion validity is how poorly athletic performance at the NFL’s combine actually predicts performance on the field on gameday. There are dozens of tests that the athletes go through, but about 99% of them have no association with how well they do in games.  

However, nutrition and exercise are highly related to longevity (the criterion). Those constructs have criterion validity because hundreds of studies have identified that nutrition and exercise are directly linked to living a longer and healthier life.

There are so many types of validity because the measurement precision of abstract concepts is hard to discern. There can also be confusion and disagreement among experts on the definition of constructs and how they should be measured.

For these reasons, social scientists have spent considerable time developing a variety of methods to assess the validity of their measurement tools. Sometimes this reveals ways to improve techniques, and sometimes it reveals the fallacy of trying to predict the future based on faulty assessment procedures.  

Cook, T.D. and Campbell, D.T. (1979) Quasi-Experimentation: Design and Analysis Issues for Field Settings. Houghton Mifflin, Boston.

Cohen, R. J., & Swerdlik, M. E. (2005). Psychological testing and assessment: An introduction to tests and measurement (6th ed.). New York: McGraw-Hill.

Cronbach, L. J. (1970). Essentials of Psychological Testing . New York: Harper & Row.

Cronbach, L. J., and Meehl, P. E. (1955) Construct validity in psychological tests. Psychological Bulletin , 52 , 281-302.

Simms, L. (2007). Classical and Modern Methods of Psychological Scale Construction. Social and Personality Psychology Compass, 2 (1), 414 – 433. https://doi.org/10.1111/j.1751-9004.2007.00044.x

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• Reliability vs Validity in Research: Types & Examples

In everyday life, we probably use reliability to describe how something is valid. However, in research and testing, reliability and validity are not the same things.

When it comes to data analysis, reliability refers to how easily replicable an outcome is. For example, if you measure a cup of rice three times, and you get the same result each time, that result is reliable.

The validity, on the other hand, refers to the measurement’s accuracy. This means that if the standard weight for a cup of rice is 5 grams, and you measure a cup of rice, it should be 5 grams.

So, while reliability and validity are intertwined, they are not synonymous. If one of the measurement parameters, such as your scale, is distorted, the results will be consistent but invalid.

Data must be consistent and accurate to be used to draw useful conclusions. In this article, we’ll look at how to assess data reliability and validity, as well as how to apply it.

Read: Internal Validity in Research: Definition, Threats, Examples

What is Reliability?

When a measurement is consistent it’s reliable. But of course, reliability doesn’t mean your outcome will be the same, it just means it will be in the same range. 

For example, if you scored 95% on a test the first time and the next you score, 96%, your results are reliable.  So, even if there is a minor difference in the outcomes, as long as it is within the error margin, your results are reliable.

Reliability allows you to assess the degree of consistency in your results. So, if you’re getting similar results, reliability provides an answer to the question of how similar your results are.

What is Validity?

A measurement or test is valid when it correlates with the expected result. It examines the accuracy of your result.

Here’s where things get tricky: to establish the validity of a test, the results must be consistent. Looking at most experiments (especially physical measurements), the standard value that establishes the accuracy of a measurement is the outcome of repeating the test to obtain a consistent result.

Read: What is Participant Bias? How to Detect & Avoid It

For example, before I can conclude that all 12-inch rulers are one foot, I must repeat the experiment several times and obtain very similar results, indicating that 12-inch rulers are indeed one foot.

Most scientific experiments are inextricably linked in terms of validity and reliability. For example, if you’re measuring distance or depth, valid answers are likely to be reliable.

But for social experiences, one isn’t the indication of the other. For example, most people believe that people that wear glasses are smart. 

Of course, I’ll find examples of people who wear glasses and have high IQs (reliability), but the truth is that most people who wear glasses simply need their vision to be better (validity). 

So reliable answers aren’t always correct but valid answers are always reliable.

How Are Reliability and Validity Assessed?

When assessing reliability, we want to know if the measurement can be replicated. Of course, we’d have to change some variables to ensure that this test holds, the most important of which are time, items, and observers.

If the main factor you change when performing a reliability test is time, you’re performing a test-retest reliability assessment.

Read: What is Publication Bias? (How to Detect & Avoid It)

However, if you are changing items, you are performing an internal consistency assessment. It means you’re measuring multiple items with a single instrument.

Finally, if you’re measuring the same item with the same instrument but using different observers or judges, you’re performing an inter-rater reliability test.

Assessing Validity

Evaluating validity can be more tedious than reliability. With reliability, you’re attempting to demonstrate that your results are consistent, whereas, with validity, you want to prove the correctness of your outcome.

Although validity is mainly categorized under two sections (internal and external), there are more than fifteen ways to check the validity of a test. In this article, we’ll be covering four.

First, content validity, measures whether the test covers all the content it needs to provide the outcome you’re expecting. 

Suppose I wanted to test the hypothesis that 90% of Generation Z uses social media polls for surveys while 90% of millennials use forms. I’d need a sample size that accounts for how Gen Z and millennials gather information.

Next, criterion validity is when you compare your results to what you’re supposed to get based on a chosen criteria. There are two ways these could be measured, predictive or concurrent validity.

Read: Survey Errors To Avoid: Types, Sources, Examples, Mitigation

Following that, we have face validity . It’s how we anticipate a test to be. For instance, when answering a customer service survey, I’d expect to be asked about how I feel about the service provided.

Lastly, construct-related validity . This is a little more complicated, but it helps to show how the validity of research is based on different findings.

As a result, it provides information that either proves or disproves that certain things are related.

Types of Reliability

We have three main types of reliability assessment and here’s how they work:

1) Test-retest Reliability

This assessment refers to the consistency of outcomes over time. Testing reliability over time does not imply changing the amount of time it takes to conduct an experiment; rather, it means repeating the experiment multiple times in a short time.

For example, if I measure the length of my hair today, and tomorrow, I’ll most likely get the same result each time. 

A short period is relative in terms of reliability; two days for measuring hair length is considered short. But that’s far too long to test how quickly water dries on the sand.

A test-retest correlation is used to compare the consistency of your results. This is typically a scatter plot that shows how similar your values are between the two experiments.

If your answers are reliable, your scatter plots will most likely have a lot of overlapping points, but if they aren’t, the points (values) will be spread across the graph.

Read: Sampling Bias: Definition, Types + [Examples]

2) Internal Consistency

It’s also known as internal reliability. It refers to the consistency of results for various items when measured on the same scale.

This is particularly important in social science research, such as surveys, because it helps determine the consistency of people’s responses when asked the same questions.

Most introverts, for example, would say they enjoy spending time alone and having few friends. However, if some introverts claim that they either do not want time alone or prefer to be surrounded by many friends, it doesn’t add up.

These people who claim to be introverts or one this factor isn’t a reliable way of measuring introversion.

Internal reliability helps you prove the consistency of a test by varying factors. It’s a little tough to measure quantitatively but you could use the split-half correlation .

The split-half correlation simply means dividing the factors used to measure the underlying construct into two and plotting them against each other in the form of a scatter plot.

Introverts, for example, are assessed on their need for alone time as well as their desire to have as few friends as possible. If this plot is dispersed, likely, one of the traits does not indicate introversion.

3) Inter-Rater Reliability

This method of measuring reliability helps prevent personal bias. Inter-rater reliability assessment helps judge outcomes from the different perspectives of multiple observers.

A good example is if you ordered a meal and found it delicious. You could be biased in your judgment for several reasons, perception of the meal, your mood, and so on.

But it’s highly unlikely that six more people would agree that the meal is delicious if it isn’t. Another factor that could lead to bias is expertise. Professional dancers, for example, would perceive dance moves differently than non-professionals. 

Read: What is Experimenter Bias? Definition, Types & Mitigation

So, if a person dances and records it, and both groups (professional and unprofessional dancers) rate the video, there is a high likelihood of a significant difference in their ratings.

But if they both agree that the person is a great dancer, despite their opposing viewpoints, the person is likely a great dancer.

Types of Validity

Researchers use validity to determine whether a measurement is accurate or not. The accuracy of measurement is usually determined by comparing it to the standard value.

When a measurement is consistent over time and has high internal consistency, it increases the likelihood that it is valid.

1) Content Validity

This refers to determining validity by evaluating what is being measured. So content validity tests if your research is measuring everything it should to produce an accurate result.

For example, if I were to measure what causes hair loss in women. I’d have to consider things like postpartum hair loss, alopecia, hair manipulation, dryness, and so on.

By omitting any of these critical factors, you risk significantly reducing the validity of your research because you won’t be covering everything necessary to make an accurate deduction. 

Read: Data Cleaning: 7 Techniques + Steps to Cleanse Data

For example, a certain woman is losing her hair due to postpartum hair loss, excessive manipulation, and dryness, but in my research, I only look at postpartum hair loss. My research will show that she has postpartum hair loss, which isn’t accurate.

Yes, my conclusion is correct, but it does not fully account for the reasons why this woman is losing her hair.

2) Criterion Validity

This measures how well your measurement correlates with the variables you want to compare it with to get your result. The two main classes of criterion validity are predictive and concurrent.

3) Predictive validity

It helps predict future outcomes based on the data you have. For example, if a large number of students performed exceptionally well in a test, you can use this to predict that they understood the concept on which the test was based and will perform well in their exams.

4) Concurrent validity

On the other hand, involves testing with different variables at the same time. For example, setting up a literature test for your students on two different books and assessing them at the same time.

You’re measuring your students’ literature proficiency with these two books. If your students truly understood the subject, they should be able to correctly answer questions about both books.

5) Face Validity

Quantifying face validity might be a bit difficult because you are measuring the perception validity, not the validity itself. So, face validity is concerned with whether the method used for measurement will produce accurate results rather than the measurement itself.

If the method used for measurement doesn’t appear to test the accuracy of a measurement, its face validity is low.

Here’s an example: less than 40% of men over the age of 20 in Texas, USA, are at least 6 feet tall. The most logical approach would be to collect height data from men over the age of twenty in Texas, USA.

However, asking men over the age of 20 what their favorite meal is to determine their height is pretty bizarre. The method I am using to assess the validity of my research is quite questionable because it lacks correlation to what I want to measure.

6) Construct-Related Validity

Construct-related validity assesses the accuracy of your research by collecting multiple pieces of evidence. It helps determine the validity of your results by comparing them to evidence that supports or refutes your measurement.

7) Convergent validity

If you’re assessing evidence that strongly correlates with the concept, that’s convergent validity . 

8) Discriminant validity

Examines the validity of your research by determining what not to base it on. You are removing elements that are not a strong factor to help validate your research. Being a vegan, for example, does not imply that you are allergic to meat.

How to Ensure Validity and Reliability in Your Research

You need a bulletproof research design to ensure that your research is both valid and reliable. This means that your methods, sample, and even you, the researcher, shouldn’t be biased.

• Ensuring Reliability

To enhance the reliability of your research, you need to apply your measurement method consistently. The chances of reproducing the same results for a test are higher when you maintain the method you’re using to experiment.

For example, you want to determine the reliability of the weight of a bag of chips using a scale. You have to consistently use this scale to measure the bag of chips each time you experiment.

You must also keep the conditions of your research consistent. For instance, if you’re experimenting to see how quickly water dries on sand, you need to consider all of the weather elements that day.

So, if you experimented on a sunny day, the next experiment should also be conducted on a sunny day to obtain a reliable result.

Read: Survey Methods: Definition, Types, and Examples

• Ensuring Validity

There are several ways to determine the validity of your research, and the majority of them require the use of highly specific and high-quality measurement methods.

Before you begin your test, choose the best method for producing the desired results. This method should be pre-existing and proven.

Also, your sample should be very specific. If you’re collecting data on how dogs respond to fear, your results are more likely to be valid if you base them on a specific breed of dog rather than dogs in general.

Validity and reliability are critical for achieving accurate and consistent results in research. While reliability does not always imply validity, validity establishes that a result is reliable. Validity is heavily dependent on previous results (standards), whereas reliability is dependent on the similarity of your results.

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Content Validity in Research: Definition & Examples

Charlotte Nickerson

Research Assistant at Harvard University

Undergraduate at Harvard University

Charlotte Nickerson is a student at Harvard University obsessed with the intersection of mental health, productivity, and design.

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Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

• Content validity is a type of criterion validity that demonstrates how well a measure covers the construct it is meant to represent.
• It is important for researchers to establish content validity in order to ensure that their study is measuring what it intends to measure.
• There are several ways to establish content validity, including expert opinion, focus groups , and surveys.

What Is Content Validity?

Content Validity is the degree to which elements of an assessment instrument are relevant to a representative of the targeted construct for a particular assessment purpose.

This encompasses aspects such as the appropriateness of the items, tasks, or questions to the specific domain being measured and whether the assessment instrument covers a broad enough range of content to enable conclusions to be drawn about the targeted construct (Rossiter, 2008).

One example of an assessment with high content validity is the Iowa Test of Basic Skills (ITBS). The ITBS is a standardized test that has been used since 1935 to assess the academic achievement of students in grades 3-8.

The test covers a wide range of academic skills, including reading, math, language arts, and social studies. The items on the test are carefully developed and reviewed by a panel of experts to ensure that they are fair and representative of the skills being tested.

As a result, the ITBS has high content validity and is widely used by schools and districts to measure student achievement.

Meanwhile, most driving tests have low content validity.  The questions on the test are often not representative of the skills needed to drive safely. For example, many driving permit tests do not include questions about how to parallel park or how to change lanes.

Meanwhile, driving license tests often do not test drivers in non-ideal conditions, such as rain or snow. As a result, these tests do not provide an accurate measure of a person’s ability to drive safely.

The higher the content validity of an assessment, the more accurately it can measure what it is intended to measure — the target construct (Rossiter, 2008).

Why is content validity important in research?

Content validity is important in research as it provides confidence that an instrument is measuring what it is supposed to be measuring.

This is particularly relevant when developing new measures or adapting existing ones for use with different populations.

It also has implications for the interpretation of results, as findings can only be accurately applied to groups for which the content validity of the measure has been established.

Step-by-step guide: How to measure content validity?

Haynes et al. (1995) emphasized the importance of content validity and gave an overview of ways to assess it.

One of the first ways of measuring content validity was the Delphi method, which was invented by NASA in 1940 as a way of systematically creating technical predictions. 

The method involves a group of experts who make predictions about the future and then reach a consensus about those predictions. Today, the Delphi method is most commonly used in medicine.

In a content validity study using the Delphi method, a panel of experts is asked to rate the items on an assessment instrument on a scale. The expert panel also has the opportunity to add comments about the items.

After all ratings have been collected, the average item rating is calculated. In the second round, the experts receive summarized results of the first round and are able to make further comments and revise their first-round answers.

This back-and-forth continues until some homogeneity criterion — similarity between the results of researchers — is achieved (Koller et al., 2017).

Lawshie (1975) and Lynn (1986) created numerical methods to assess content validity. Both of these methods require the development of a content validity index (CVI). A content validity index is a statistical measure of the degree to which an assessment instrument covers the content domain of interest.

There are two steps in calculating a content validity index:

• Determining the number of items that should be included in the assessment instrument;
• Determining the percentage of items that actually are included in the assessment instrument.

The first step, determining the number of items that should be included in an assessment instrument, can be done using one of two approaches: item sampling or expert consensus.

Item sampling involves selecting a sample of items from a larger set of items that cover the content domain. The number of items in the sample is then used to estimate the total number of items needed to cover the content domain.

This approach has the advantage of being quick and easy, but it can be biased if the sample of items is not representative of the larger set (Koller et al., 2017).

The second approach, expert consensus, involves asking a group of experts how many items should be included in an assessment instrument to adequately cover the content domain. This approach has the advantage of being more objective, but it can be time-consuming and expensive.

Experts are able to assign these items to dimensions of the construct that they intend to measure and assign relevance values to decide whether an item is a strong measure of the construct.

Although various attempts to numerize the process of measuring content validity exist, there is no systematic procedure that could be used as a general guideline for the evaluation of content validity (Newman et al., 2013).

When is content validity used?

Education assessment.

In the context of educational assessment, validity is the extent to which an assessment instrument accurately measures what it is intended to measure. Validity concerns anyone who is making inferences and decisions about a learner based on data.

This can have deep implications for students’ education and future. For instance, a test that poorly measures students’ abilities can lead to placement in a future course that is unsuitable for the student and, ultimately, to the student’s failure (Obilor, 2022).

There are a number of factors that specifically affect the validity of assessments given to students, such as (Obilor, 2018):

• Unclear Direction: If directions do not clearly indicate to the respondent how to respond to the tool’s items, the validity of the tool is reduced.
• Vocabulary: If the vocabulary of the respondent is poor, and he does not understand the items, the validity of the instrument is affected.
• Poorly Constructed Test Items: If items are constructed in such a way that they have different meanings for different respondents, validity is affected.
• Difficulty Level of Items: In an achievement test, too easy or too difficult test items would not discriminate among students, thereby lowering the validity of the test.
• Influence of Extraneous Factors: Extraneous factors like the style of expression, legibility, mechanics of grammar (spelling, punctuation), handwriting, and length of the tool, amongst others, influence the validity of a tool.
• Inappropriate Time Limit: In a speed test, if enough time limit is given, the result will be invalidated as a measure of speed. In a power test, an inappropriate time limit will lower the validity of the test.

There are a few reasons why interviews may lack content validity . First, interviewers may ask different questions or place different emphases on certain topics across different candidates. This can make it difficult to compare candidates on a level playing field.

Second, interviewers may have their own personal biases that come into play when making judgments about candidates.

Finally, the interview format itself may be flawed. For example, many companies ask potential programmers to complete brain teasers — such as calculating the number of plumbers in Chicago or coding tasks that rely heavily on theoretical knowledge of data structures — even if this knowledge would be used rarely or never on the job.

Questionnaires

Questionnaires rely on the respondents’ ability to accurately recall information and report it honestly. Additionally, the way in which questions are worded can influence responses.

To increase content validity when designing a questionnaire, careful consideration must be given to the types of questions that will be asked.

Open-ended questions are typically less biased than closed-ended questions, but they can be more difficult to analyze.

It is also important to avoid leading or loaded questions that might influence respondents’ answers in a particular direction. The wording of questions should be clear and concise to avoid confusion (Koller et al., 2017).

Is content validity internal or external?

Most experts agree that content validity is primarily an internal issue. This means that the concepts and items included in a test should be based on a thorough analysis of the specific content area being measured.

The items should also be representative of the range of difficulty levels within that content area. External factors, such as the opinions of experts or the general public, can influence content validity, but they are not necessarily the primary determinant.

In some cases, such as when developing a test for licensure or certification, external stakeholders may have a strong say in what is included in the test (Koller et al., 2017).

How can content validity be improved?

There are a few ways to increase content validity. One is to create items that are more representative of the targeted construct. Another is to increase the number of items on the assessment so that it covers a greater range of content.

Finally, experts can review the items on the assessment to ensure that they are fair and representative of the skills being tested (Koller et al., 2017).

How do you test the content validity of a questionnaire?

There are a few ways to test the content validity of a questionnaire. One way is to ask experts in the field to review the questions and provide feedback on whether or not they believe the questions are relevant and cover all important topics.

Another way is to administer the questionnaire to a small group of people and then analyze the results to see if there are any patterns or themes emerging from the responses.

Finally, it is also possible to use statistical methods to test for content validity, although this approach is more complex and usually requires access to specialized software (Koller et al., 2017).

How can you tell if an instrument is content-valid?

There are a few ways to tell if an instrument is content-valid. The first of these involves looking at two subsets of content validity: face and construct validity.

Face validity is a measure of whether or not the items on the test appear to measure what they claim to measure. This is highly subjective but convenient to assess.

Another way is to look at the construct validity, which is whether or not the items on the test measure what they are supposed to measure. Finally, you can also look at the criterion-related validity, which is whether or not the items on the test predict future performance.

What is the difference between content and criterion validity?

Content validity is a measure of how well a test covers the content it is supposed to cover.

Criterion validity, meanwhile, is an index of how well a test correlates with an established standard of comparison or a criterion.

For example, if a measure of criminal behavior is criterion valid, then it should be possible to use it to predict whether an individual will be arrested in the future for a criminal violation, is currently breaking the law, and has a previous criminal record (American Psychological Association).

Are content validity and construct validity the same?

Content validity is not the same as construct validity.

Content validity is a method of assessing the degree to which a measure covers the range of content that it purports to measure.

In contrast, construct validity is a method of assessing the degree to which a measure reflects the underlying construct that it purports to measure.

It is important to note that content validity and construct validity are not mutually exclusive; a measure can be both valid and invalid with respect to content and construct.

However, content validity is a necessary but not sufficient condition for construct validity. That is, a measure cannot be construct valid if it does not first have content validity (Koller et al., 2017).

For example, an academic achievement test in math may have content validity if it contains questions from all areas of math a student is expected to have learned before the test, but it may not have construct validity if it does not somehow relate to tests of similar and different constructs.

How many experts are needed for content validity?

There is no definitive answer to this question as it depends on a number of factors, including the nature of the instrument being validated and the purpose of the validation exercise.

However, in general, a minimum of three experts should be used in order to ensure that the content validity of an instrument is adequately established (Koller et al., 2017).

American Psychological Association. (n.D.). Content Validity. American Psychological Association Dictionary.

Haynes, S. N., Richard, D., & Kubany, E. S. (1995). Content validity in psychological assessment: A functional approach to concepts and methods. Psychological assessment , 7 (3), 238.

Koller, I., Levenson, M. R., & Glück, J. (2017). What do you think you are measuring? A mixed-methods procedure for assessing the content validity of test items and theory-based scaling. Frontiers in psychology , 8 , 126.

Lawshe, C. H. (1975). A quantitative approach to content validity. Personnel psychology , 28 (4), 563-575.

Lynn, M. R. (1986). Determination and quantification of content validity. Nursing research .

Obilor, E. I. (2018). Fundamentals of research methods and Statistics in Education and Social Sciences. Port Harcourt: SABCOS Printers & Publishers.

OBILOR, E. I. P., & MIWARI, G. U. P. (2022). Content Validity in Educational Assessment.

Newman, Isadore, Janine Lim, and Fernanda Pineda. “Content validity using a mixed methods approach: Its application and development through the use of a table of specifications methodology.” Journal of Mixed Methods Research 7.3 (2013): 243-260.

Rossiter, J. R. (2008). Content validity of measures of abstract constructs in management and organizational research. British Journal of Management , 19 (4), 380-388.

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Internal Validity vs. External Validity in Research

What they tell us about the meaningfulness and trustworthiness of research

Verywell / Bailey Mariner

• Internal Validity
• External Validity

How do you determine whether a psychology study is trustworthy and meaningful? Two characteristics that can help you assess research findings are internal and external validity.

• Internal validity measures how well a study is conducted (its structure) and how accurately its results reflect the studied group.
• External validity relates to how applicable the findings are in the real world.

These two concepts help researchers gauge if the results of a research study are trustworthy and meaningful.

Conclusions are warranted

Controls extraneous variables

Eliminates alternative explanations

Focus on accuracy and strong research methods

Findings can be generalized

Outcomes apply to practical situations

Results apply to the world at large

Results can be translated into another context

What Is Internal Validity in Research?

Internal validity is the extent to which a research study establishes a trustworthy cause-and-effect relationship. This type of validity depends largely on the study's procedures and how rigorously it is performed.

Internal validity is important because once established, it makes it possible to eliminate alternative explanations for a finding. If you implement a smoking cessation program, for instance, internal validity ensures that any improvement in the subjects is due to the treatment administered and not something else.

Internal validity is not a "yes or no" concept. Instead, we consider how confident we can be with study findings based on whether the research avoids traps that may make those findings questionable. The less chance there is for "confounding," the higher the internal validity and the more confident we can be.

Confounding refers to uncontrollable variables that come into play and can confuse the outcome of a study, making us unsure of whether we can trust that we have identified the cause-and-effect relationship.

In short, you can only be confident that a study is internally valid if you can rule out alternative explanations for the findings. Three criteria are required to assume cause and effect in a research study:

• The cause preceded the effect in terms of time.
• The cause and effect vary together.
• There are no other likely explanations for the relationship observed.

Factors That Improve Internal Validity

To ensure the internal validity of a study, you want to consider aspects of the research design that will increase the likelihood that you can reject alternative hypotheses. Many factors can improve internal validity in research, including:

• Blinding : Participants—and sometimes researchers—are unaware of what intervention they are receiving (such as using a placebo on some subjects in a medication study) to avoid having this knowledge bias their perceptions and behaviors, thus impacting the study's outcome
• Experimental manipulation : Manipulating an independent variable in a study (for instance, giving smokers a cessation program) instead of just observing an association without conducting any intervention (examining the relationship between exercise and smoking behavior)
• Random selection : Choosing participants at random or in a manner in which they are representative of the population that you wish to study
• Randomization or random assignment : Randomly assigning participants to treatment and control groups, ensuring that there is no systematic bias between the research groups
• Strict study protocol : Following specific procedures during the study so as not to introduce any unintended effects; for example, doing things differently with one group of study participants than you do with another group

Internal Validity Threats

Just as there are many ways to ensure internal validity, a list of potential threats should be considered when planning a study.

• Attrition : Participants dropping out or leaving a study, which means that the results are based on a biased sample of only the people who did not choose to leave (and possibly who all have something in common, such as higher motivation)
• Confounding : A situation in which changes in an outcome variable can be thought to have resulted from some type of outside variable not measured or manipulated in the study
• Diffusion : This refers to the results of one group transferring to another through the groups interacting and talking with or observing one another; this can also lead to another issue called resentful demoralization, in which a control group tries less hard because they feel resentful over the group that they are in
• Experimenter bias : An experimenter behaving in a different way with different groups in a study, which can impact the results (and is eliminated through blinding)
• Historical events : May influence the outcome of studies that occur over a period of time, such as a change in the political leader or a natural disaster that occurs, influencing how study participants feel and act
• Instrumentation : This involves "priming" participants in a study in certain ways with the measures used, causing them to react in a way that is different than they would have otherwise reacted
• Maturation : The impact of time as a variable in a study; for example, if a study takes place over a period of time in which it is possible that participants naturally change in some way (i.e., they grew older or became tired), it may be impossible to rule out whether effects seen in the study were simply due to the impact of time
• Statistical regression : The natural effect of participants at extreme ends of a measure falling in a certain direction due to the passage of time rather than being a direct effect of an intervention
• Testing : Repeatedly testing participants using the same measures influences outcomes; for example, if you give someone the same test three times, it is likely that they will do better as they learn the test or become used to the testing process, causing them to answer differently

What Is External Validity in Research?

External validity refers to how well the outcome of a research study can be expected to apply to other settings. This is important because, if external validity is established, it means that the findings can be generalizable to similar individuals or populations.

External validity affirmatively answers the question: Do the findings apply to similar people, settings, situations, and time periods?

Population validity and ecological validity are two types of external validity. Population validity refers to whether you can generalize the research outcomes to other populations or groups. Ecological validity refers to whether a study's findings can be generalized to additional situations or settings.

Another term called transferability refers to whether results transfer to situations with similar characteristics. Transferability relates to external validity and refers to a qualitative research design.

Factors That Improve External Validity

If you want to improve the external validity of your study, there are many ways to achieve this goal. Factors that can enhance external validity include:

• Field experiments : Conducting a study outside the laboratory, in a natural setting
• Inclusion and exclusion criteria : Setting criteria as to who can be involved in the research, ensuring that the population being studied is clearly defined
• Psychological realism : Making sure participants experience the events of the study as being real by telling them a "cover story," or a different story about the aim of the study so they don't behave differently than they would in real life based on knowing what to expect or knowing the study's goal
• Replication : Conducting the study again with different samples or in different settings to see if you get the same results; when many studies have been conducted on the same topic, a meta-analysis can also be used to determine if the effect of an independent variable can be replicated, therefore making it more reliable
• Reprocessing or calibration : Using statistical methods to adjust for external validity issues, such as reweighting groups if a study had uneven groups for a particular characteristic (such as age)

External Validity Threats

External validity is threatened when a study does not take into account the interaction of variables in the real world. Threats to external validity include:

• Pre- and post-test effects : When the pre- or post-test is in some way related to the effect seen in the study, such that the cause-and-effect relationship disappears without these added tests
• Sample features : When some feature of the sample used was responsible for the effect (or partially responsible), leading to limited generalizability of the findings
• Selection bias : Also considered a threat to internal validity, selection bias describes differences between groups in a study that may relate to the independent variable—like motivation or willingness to take part in the study, or specific demographics of individuals being more likely to take part in an online survey
• Situational factors : Factors such as the time of day of the study, its location, noise, researcher characteristics, and the number of measures used may affect the generalizability of findings

While rigorous research methods can ensure internal validity, external validity may be limited by these methods.

Internal Validity vs. External Validity

Internal validity and external validity are two research concepts that share a few similarities while also having several differences.

Similarities

One of the similarities between internal validity and external validity is that both factors should be considered when designing a study. This is because both have implications in terms of whether the results of a study have meaning.

Both internal validity and external validity are not "either/or" concepts. Therefore, you always need to decide to what degree a study performs in terms of each type of validity.

Each of these concepts is also typically reported in research articles published in scholarly journals . This is so that other researchers can evaluate the study and make decisions about whether the results are useful and valid.

Differences

The essential difference between internal validity and external validity is that internal validity refers to the structure of a study (and its variables) while external validity refers to the universality of the results. But there are further differences between the two as well.

For instance, internal validity focuses on showing a difference that is due to the independent variable alone. Conversely, external validity results can be translated to the world at large.

Internal validity and external validity aren't mutually exclusive. You can have a study with good internal validity but be overall irrelevant to the real world. You could also conduct a field study that is highly relevant to the real world but doesn't have trustworthy results in terms of knowing what variables caused the outcomes.

Examples of Validity

Perhaps the best way to understand internal validity and external validity is with examples.

Internal Validity Example

An example of a study with good internal validity would be if a researcher hypothesizes that using a particular mindfulness app will reduce negative mood. To test this hypothesis, the researcher randomly assigns a sample of participants to one of two groups: those who will use the app over a defined period and those who engage in a control task.

The researcher ensures that there is no systematic bias in how participants are assigned to the groups. They do this by blinding the research assistants so they don't know which groups the subjects are in during the experiment.

A strict study protocol is also used to outline the procedures of the study. Potential confounding variables are measured along with mood , such as the participants' socioeconomic status, gender, age, and other factors. If participants drop out of the study, their characteristics are examined to make sure there is no systematic bias in terms of who stays in.

External Validity Example

An example of a study with good external validity would be if, in the above example, the participants used the mindfulness app at home rather than in the laboratory. This shows that results appear in a real-world setting.

To further ensure external validity, the researcher clearly defines the population of interest and chooses a representative sample . They might also replicate the study's results using different technological devices.

Setting up an experiment so that it has both sound internal validity and external validity involves being mindful from the start about factors that can influence each aspect of your research.

It's best to spend extra time designing a structurally sound study that has far-reaching implications rather than to quickly rush through the design phase only to discover problems later on. Only when both internal validity and external validity are high can strong conclusions be made about your results.

Andrade C. Internal, external, and ecological validity in research design, conduct, and evaluation .  Indian J Psychol Med . 2018;40(5):498-499. doi:10.4103/IJPSYM.IJPSYM_334_18

San Jose State University. Internal and external validity .

Kemper CJ. Internal validity . In: Zeigler-Hill V, Shackelford TK, eds. Encyclopedia of Personality and Individual Differences . Springer International Publishing; 2017:1-3. doi:10.1007/978-3-319-28099-8_1316-1

Patino CM, Ferreira JC. Internal and external validity: can you apply research study results to your patients?   J Bras Pneumol . 2018;44(3):183. doi:10.1590/S1806-37562018000000164

Matthay EC, Glymour MM. A graphical catalog of threats to validity: Linking social science with epidemiology .  Epidemiology . 2020;31(3):376-384. doi:10.1097/EDE.0000000000001161

Amico KR. Percent total attrition: a poor metric for study rigor in hosted intervention designs .  Am J Public Health . 2009;99(9):1567-1575. doi:10.2105/AJPH.2008.134767

Kemper CJ. External validity . In: Zeigler-Hill V, Shackelford TK, eds. Encyclopedia of Personality and Individual Differences . Springer International Publishing; 2017:1-4. doi:10.1007/978-3-319-28099-8_1303-1

Desjardins E, Kurtz J, Kranke N, Lindeza A, Richter SH. Beyond standardization: improving external validity and reproducibility in experimental evolution . BioScience. 2021;71(5):543-552. doi:10.1093/biosci/biab008

Drude NI, Martinez Gamboa L, Danziger M, Dirnagl U, Toelch U. Improving preclinical studies through replications .  Elife . 2021;10:e62101. doi:10.7554/eLife.62101

Michael RS. Threats to internal & external validity: Y520 strategies for educational inquiry .

Pahus L, Burgel PR, Roche N, Paillasseur JL, Chanez P. Randomized controlled trials of pharmacological treatments to prevent COPD exacerbations: applicability to real-life patients . BMC Pulm Med . 2019;19(1):127. doi:10.1186/s12890-019-0882-y

By Arlin Cuncic, MA Arlin Cuncic, MA, is the author of The Anxiety Workbook and founder of the website About Social Anxiety. She has a Master's degree in clinical psychology.

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Reliability vs. Validity in Research: Types & Examples

When it comes to research, getting things right is crucial. That’s where the concepts of “Reliability vs Validity in Research” come in. 

Imagine it like a balancing act – making sure your measurements are consistent and accurate at the same time. This is where test-retest reliability, having different researchers check things, and keeping things consistent within your research plays a big role. 

As we dive into this topic, we’ll uncover the differences between reliability and validity, see how they work together, and learn how to use them effectively.

Understanding Reliability vs. Validity in Research

When it comes to collecting data and conducting research, two crucial concepts stand out: reliability and validity. 

These pillars uphold the integrity of research findings, ensuring that the data collected and the conclusions drawn are both meaningful and trustworthy. Let’s dive into the heart of the concepts, reliability, and validity, to comprehend their significance in the realm of research truly.

What is reliability?

Reliability refers to the consistency and dependability of the data collection process. It’s like having a steady hand that produces the same result each time it reaches for a task. 

In the research context, reliability is all about ensuring that if you were to repeat the same study using the same reliable measurement technique, you’d end up with the same results. It’s like having multiple researchers independently conduct the same experiment and getting outcomes that align perfectly.

Imagine you’re using a thermometer to measure the temperature of the water. You have a reliable measurement if you dip the thermometer into the water multiple times and get the same reading each time. This tells you that your method and measurement technique consistently produce the same results, whether it’s you or another researcher performing the measurement.

What is validity?

On the other hand, validity refers to the accuracy and meaningfulness of your data. It’s like ensuring that the puzzle pieces you’re putting together actually form the intended picture. When you have validity, you know that your method and measurement technique are consistent and capable of producing results aligned with reality.

Think of it this way; Imagine you’re conducting a test that claims to measure a specific trait, like problem-solving ability. If the test consistently produces results that accurately reflect participants’ problem-solving skills, then the test has high validity. In this case, the test produces accurate results that truly correspond to the trait it aims to measure.

In essence, while reliability assures you that your data collection process is like a well-oiled machine producing the same results, validity steps in to ensure that these results are not only consistent but also relevantly accurate. 

Together, these concepts provide researchers with the tools to conduct research that stands on a solid foundation of dependable methods and meaningful insights.

Types of Reliability

Let’s explore the various types of reliability that researchers consider to ensure their work stands on solid ground.

High test-retest reliability

Test-retest reliability involves assessing the consistency of measurements over time. It’s like taking the same measurement or test twice – once and then again after a certain period. If the results align closely, it indicates that the measurement is reliable over time. Think of it as capturing the essence of stability. 

Inter-rater reliability

When multiple researchers or observers are part of the equation, interrater reliability comes into play. This type of reliability assesses the level of agreement between different observers when evaluating the same phenomenon. It’s like ensuring that different pairs of eyes perceive things in a similar way. 

Internal reliability

Internal consistency dives into the harmony among different items within a measurement tool aiming to assess the same concept. This often comes into play in surveys or questionnaires, where participants respond to various items related to a single construct. If the responses to these items consistently reflect the same underlying concept, the measurement is said to have high internal consistency. 

Types of validity

Let’s explore the various types of validity that researchers consider to ensure their work stands on solid ground.

Content validity

It delves into whether a measurement truly captures all dimensions of the concept it intends to measure. It’s about making sure your measurement tool covers all relevant aspects comprehensively. 

Imagine designing a test to assess students’ understanding of a history chapter. It exhibits high content validity if the test includes questions about key events, dates, and causes. However, if it focuses solely on dates and omits causation, its content validity might be questionable.

Construct validity

It assesses how well a measurement aligns with established theories and concepts. It’s like ensuring that your measurement is a true representation of the abstract construct you’re trying to capture. 

Criterion validity

Criterion validity examines how well your measurement corresponds to other established measurements of the same concept. It’s about making sure your measurement accurately predicts or correlates with external criteria.

Differences between reliability and validity in research

Let’s delve into the differences between reliability and validity in research.

While both reliability and validity contribute to trustworthy research, they address distinct aspects. Reliability ensures consistent results, while validity ensures accurate and relevant results that reflect the true nature of the measured concept.

Example of Reliability and Validity in Research

In this section, we’ll explore instances that highlight the differences between reliability and validity and how they play a crucial role in ensuring the credibility of research findings.

Example of reliability

Imagine you are studying the reliability of a smartphone’s battery life measurement. To collect data, you fully charge the phone and measure the battery life three times in the same controlled environment—same apps running, same brightness level, and same usage patterns. 

If the measurements consistently show a similar battery life duration each time you repeat the test, it indicates that your measurement method is reliable. The consistent results under the same conditions assure you that the battery life measurement can be trusted to provide dependable information about the phone’s performance.

Example of validity

Researchers collect data from a group of participants in a study aiming to assess the validity of a newly developed stress questionnaire. To ensure validity, they compare the scores obtained from the stress questionnaire with the participants’ actual stress levels measured using physiological indicators such as heart rate variability and cortisol levels. 

If participants’ scores correlate strongly with their physiological stress levels, the questionnaire is valid. This means the questionnaire accurately measures participants’ stress levels, and its results correspond to real variations in their physiological responses to stress. 

Validity assessed through the correlation between questionnaire scores and physiological measures ensures that the questionnaire is effectively measuring what it claims to measure participants’ stress levels.

In the world of research, differentiating between reliability and validity is crucial. Reliability ensures consistent results, while validity confirms accurate measurements. Using tools like QuestionPro enhances data collection for both reliability and validity. For instance, measuring self-esteem over time showcases reliability, and aligning questions with theories demonstrates validity. 

QuestionPro empowers researchers to achieve reliable and valid results through its robust features, facilitating credible research outcomes. Contact QuestionPro to create a free account or learn more!

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Reliability and Validity in Research: Definitions, Examples

Statistics Definitions > Reliability and Validity

What is Reliability?

The reliability coefficient, what is validity.

• Curricular Validity

Overview of Reliability and Validity

Outside of statistical research, reliability and validity are used interchangeably. For research and testing, there are subtle differences. Reliability implies consistency: if you take the ACT five times, you should get roughly the same results every time. A test is valid if it measures what it’s supposed to.

Tests that are valid are also reliable. The ACT is valid (and reliable) because it measures what a student learned in high school. However, tests that are reliable aren’t always valid. For example, let’s say your thermometer was a degree off. It would be reliable (giving you the same results each time) but not valid (because the thermometer wasn’t recording the correct temperature).

Reliability is a measure of the stability or consistency of test scores. You can also think of it as the ability for a test or research findings to be repeatable. For example, a medical thermometer is a reliable tool that would measure the correct temperature each time it is used. In the same way, a reliable math test will accurately measure mathematical knowledge for every student who takes it and reliable research findings can be replicated over and over.

Of course, it’s not quite as simple as saying you think a test is reliable. There are many statistical tools you can use to measure reliability. For example:

• Kuder-Richardson 20 : a measure of internal reliability for a binary test (i.e. one with right or wrong answers).
• Cronbach’s alpha : measures internal reliability for tests with multiple possible answers.

Internal vs. External Reliability

Internal reliability , or internal consistency, is a measure of how well your test is actually measuring what you want it to measure. External reliability means that your test or measure can be generalized beyond what you’re using it for. For example, a claim that individual tutoring improves test scores should apply to more than one subject (e.g. to English as well as math). A test for depression should be able to detect depression in different age groups, for people in different socio-economic statuses, or introverts.

One specific type is parallel forms reliability , where two equivalent tests are given to students a short time apart. If the forms are parallel, then the tests produce the same observed results.

A reliability coefficient is a measure of how well a test measures achievement. It is the proportion of variance in observed scores (i.e. scores on the test) attributable to true scores (the theoretical “real” score that a person would get if a perfect test existed).

The term “reliability coefficient” actually refers to several different coefficients : Several methods exist for calculating the coefficient include test-retest, parallel forms and alternate-form :

• Cronbach’s alpha — the most widely used internal-consistency coefficient.
• A simple correlation between two scores from the same person is one of the simplest ways to estimate a reliability coefficient. If the scores are taken at different times, then this is one way to estimate test-retest reliability ; Different forms of the test given on the same day can estimate parallel forms reliability .
• Pearson’s correlation can be used to estimate the theoretical reliability coefficient between parallel tests.
• The Spearman Brown formula is a measure of reliability for split-half tests.
• Cohen’s Kappa measures interrater reliability.

The range of the reliability coefficient is from 0 to 1. Rule of thumb for preferred levels of the coefficient:

• For high stakes tests (e.g. college admissions), > 0.85. Some authors suggest this figure should be above .90.
• For low stakes tests (e.g. classroom assessment), > 0.70. Some authors suggest this figure should be above 0.80

• Composite Reliability
• Concurrent Validity .
• Content Validity .
• Convergent Validity .
• Consequential Validity.
• Criterion Validity.
• Curricular Validity and Instructional Validity .
• Ecological Validity .
• External Validity .
• Face Validity .
• Formative validity & Summative Validity.
• Incremental Validity
• Internal Validity .
• Predictive Validity .
• Sampling Validity.
• Statistical Conclusion Validity .

What is Curricular Validity?

Validity is defined by how well a test measures what it’s supposed to measure. Curricular validity refers to how well test items reflect the actual curriculum (i.e. a test is supposed to be a measure of what’s on the curriculum). It usually refers to a specific, well-defined curriculum, like those provided by states to schools. McClung (1978) defines it as

“…a measure of how well test items represent the objectives of the curriculum”.

A similar term is instructional validity, which is how well the test items reflect what is actually taught . McClung defines instructional validity as “an actual measure of whether the schools are providing students with instruction in the knowledge and skills measured by the test.”

In an ideal educational world, there would be no need for a distinction between instructional and curricular validity: teachers follow a curriculum, students learn what is on the curriculum through their teachers. However, it doesn’t always follow that a child will be taught what is on the curriculum. Many things can have an impact on what parts of the curriculum are taught (or not taught), including:

• Inexperienced teachers,
• Substitute teachers,
• Poorly managed schools/flow of information,
• Teachers may choose not to teach specific parts of the curriculum they don’t agree with (e.g. evolution or sex education ),
• Teachers might skip over parts of the curriculum they don’t fully understand (like mathematics. According to this report , elementary school teachers struggle with basic math concepts).

How to Measure Curricular Validity

Curricular validity is usually measured by a panel of curriculum experts. It’s not measured statistically, but rather by a rating of “valid” or “not valid.” A test that meets one definition of validity might not meet another. For example, a test might have curricular validity, but not instructional validity and vice versa.

References : McClung, M. S. (1978). Competency testing programs: Legal and educational issues. Fordham Law Review, 47, 651-712. Ostashevsky, L. (2016). Elementary school teachers struggle with Common Core math standards.

Everitt, B. S.; Skrondal, A. (2010), The Cambridge Dictionary of Statistics , Cambridge University Press. Gonick, L. (1993). The Cartoon Guide to Statistics . HarperPerennial.

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Methodology

• Internal Validity in Research | Definition, Threats, & Examples

Internal Validity in Research | Definition, Threats & Examples

Published on May 1, 2020 by Pritha Bhandari . Revised on June 22, 2023.

Internal validity is the extent to which you can be confident that a cause-and-effect relationship established in a study cannot be explained by other factors.

Table of contents

Why internal validity matters, how to check whether your study has internal validity, trade-off between internal and external validity, threats to internal validity and how to counter them, other interesting articles, frequently asked questions about internal validity.

Internal validity makes the conclusions of a causal relationship credible and trustworthy. Without high internal validity, an experiment cannot demonstrate a causal link between two variables.

Once they arrive at the laboratory, the treatment group participants are given a cup of coffee to drink, while control group participants are given water. You also give both groups memory tests. After analyzing the results, you find that the treatment group performed better than the control group on the memory test.

For your conclusion to be valid, you need to be able to rule out other explanations (including control , extraneous , and confounding variables) for the results.

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There are three necessary conditions for internal validity. All three conditions must occur to experimentally establish causality between an independent variable A (your treatment variable) and dependent variable B (your response variable).

• Your treatment and response variables change together.
• Your treatment precedes changes in your response variables
• No confounding or extraneous factors can explain the results of your study.

In the research example above, only two out of the three conditions have been met.

• Drinking coffee and memory performance increased together.
• Drinking coffee happened before the memory test.
• The time of day of the sessions is an extraneous factor that can equally explain the results of the study.

Because you assigned participants to groups based on the schedule, the groups were different at the start of the study. Any differences in memory performance may be due to a difference in the time of day. Therefore, you cannot say for certain whether the time of day or drinking a cup of coffee improved memory performance.

That means your study has low internal validity, and you cannot deduce a causal relationship between drinking coffee and memory performance.

External validity is the extent to which you can generalize the findings of a study to other measures, settings or groups. In other words, can you apply the findings of your study to a broader context?

There is an inherent trade-off between internal and external validity ; the more you control extraneous factors in your study, the less you can generalize your findings to a broader context.

Threats to internal validity are important to recognize and counter in a research design for a robust study. Different threats can apply to single-group and multi-group studies.

Single-group studies

How to counter threats in single-group studies

Altering the experimental design can counter several threats to internal validity in single-group studies.

• Adding a comparable control group counters threats to single-group studies. If comparable control and treatment groups each face the same threats, the outcomes of the study won’t be affected by them.
• A large sample size counters testing, because results would be more sensitive to any variability in the outcomes and less likely to suffer from sampling bias .
• Using filler-tasks or questionnaires to hide the purpose of study also counters testing threats and demand characteristics .

Multi-group studies

How to counter threats in multi-group studies

Altering the experimental design can counter several threats to internal validity in multi-group studies.

• Random assignment of participants to groups counters selection bias and regression to the mean by making groups comparable at the start of the study.
• Blinding participants to the aim of the study counters the effects of social interaction.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

I nternal validity is the degree of confidence that the causal relationship you are testing is not influenced by other factors or variables .

External validity is the extent to which your results can be generalized to other contexts.

The validity of your experiment depends on your experimental design .

There are eight threats to internal validity : history, maturation, instrumentation, testing, selection bias , regression to the mean, social interaction and attrition .

Attrition bias is a threat to internal validity . In experiments, differential rates of attrition between treatment and control groups can skew results.

This bias can affect the relationship between your independent and dependent variables . It can make variables appear to be correlated when they are not, or vice versa .

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Validity: Types & Examples

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Research validity concerns the degree to which a study accurately represents what it aims to investigate. It addresses the credibility and relevance of the research and its outcomes.

There are primarily two types of validity: 

• Internal validity: It ensures the research design and its execution accurately reflect the cause-and-effect relationship being studied.
• External validity : It relates to how well the study's results can be generalized to other settings, populations, or circumstances.

Are you running a research project and want to ensure its validity? We are here to help you! In this blog post, we will shed more light on every aspect of this important criteria. Get ready to learn everything about test accuracy and its types. We will cover different cases and tell you how to determine whether your research is valid. This article is jam-packed with examples so you can fully understand how things work. Shall we get started?

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What Is Validity: Definition

Validity in research is an estimate that shows how precisely your measurement method works. In other words, it tells whether the study outcomes are accurate and can be applied to the real-world setting. Research accuracy is usually considered in quantitative studies. For instance, research aimed at examining aggression in teens but which, in fact, measures low self-esteem will be invalid. Your research will only be accurate if the tool or method you are studying measures exactly what it is expected to measure.  Unlike reliability, here results shouldn’t necessarily be consistent in similar situations. However, you should pay attention to other important aspects. We will cover them in detail down below. Also read and find out our blog about validity vs reliability . You will get more facts for a better understanding.

Types of Validity

There are many various types of validity . They fall into two main categories:

• Experimental.

Each of these categories are different depending on what they are designed to identify. Let’s begin with explaining the classical definitions of these groups. Expect to find great examples to get a complete picture about the different types of research accuracy. 

Test Validity

Above we have mentioned that your research should have accurate methods of measurement and broad generalisability to be valid. And while the latter is related to the experimental studies (more on this later), the former is the main focus of a test validity.  In a nutshell, a test validity is the degree to which any test applied in research correctly measures the target object or phenomena. It is usually used in psychological or educational tests. It tells how much your supporting evidence and theory prove the interpretation of your test outcomes. Below we will discuss the primary types you may encounter while measuring the accuracy of your test. Each of these types focuses on different aspects of research precision.

Construct Validity: Definition

Construct validity allows us to find out if an instrument used for measurement is actually what we're trying to measure. It's the most important factor in determining the general accuracy of a method. A construct is any feature or trait that researchers can’t examine. But it can be easily assessed through observation of other indicators connected with it.  Constructs may refer to the characteristics of people, such as intelligence, weight, or anxiety. They could also imply larger concepts that apply to social or business groups. For example, these can be race inequality or corporate sustainability.  Construct validity example 

There aren’t any exact metrics that can help you measure aggression. However, you can rely on the related symptoms such as agitation and frequent irritability. To ensure construct accuracy, you should build a questionnaire that will help you assess the construct of aggression, but not the other constructs. 

Content Validity: Definition

Now you may wonder what content validity is. Content validity determines the degree to which a test can represent all characteristics of a construct. In order to get an accurate outcome, the material used in assessment should consider all related aspects of the subject matter under the test. If certain aspects are not included in the measurements or when inapplicable elements are integrated, then the accuracy of such method is vulnerable.  Content validity example

You are designing a test in psychology to identify whether students understood how social cognition works. The test should cover every aspect of this construct. If any details are missing, then such results might not fully represent an overall understanding. Likewise, if you fail to include relevant details emphasized during your course, the test outcomes will also be invalid.

Face Validity: Definition

Face validity, also known as logical, is the extent to which a subjective measurement of content relevancy is accurate. Here, experts need to provide their opinion on whether a method assesses any phenomenon intended. This estimate is more personal and, thus, can be prone to prejudice. However, it’s a good measurement instrument if you are doing a preliminary assessment.  Face validity example

You are studying how post-traumatic stress disorder develops. You review a questionnaire where most questions are focused on the stages of shock after experiencing some traumatic event. On the face of it, this questionnaire seems to be valid. 

Criterion Validity: Definition

A final measure of accuracy is criterion related validity. It shows how well your test represents or predicts a criterion. Here, you should understand what a criterion variable is. So let’s sort these things out. A criterion variable is something that is being predicted in your study. It’s otherwise called a response variable or a dependent variable. Criterion variables are usually considered valid.  To determine criterion accuracy, you need to compare your test outcomes with the criterion variable (the one that is believed to be true). If your results differ from this criterion, then your test is invalid.   Criterion validity example

You want to identify whether the hours students study affects a criterion variable – academic performance. If your test’s outcomes are similar to an already established criterion, then your test has a decent criterion validity.

There are three types of criterion accuracy:

• Postdictive.

We will cover the two fist types down below as they are rather widely used in research.

Predictive Validity: Definition

Predictive validity is an estimate that shows whether the test accurately predicts what it intends to predict. For example, you may want to know whether your prediction of any phenomena or human behavior is precise. Accordingly, if your assumptions are justified over time, this indicates that your measurement method has a high predictive accuracy.   Example of predictive validity

A good example of this estimate, will be any test showing academic performance at school. You predict how precisely this method will measure future performance.

Concurrent Validity: Definition

Concurrent validity, as its name suggests, shows how accurate the results are if the information about a predictor and criterion are obtained simultaneously. It can also mean the situation when one test is substituted with another test. This way, researchers can stay on budget. Concurrent validity example

A great example of this estimate is a written English test that replaces an in-person examination with a teacher. Imagine that you want to assess academic success of thousands of students. One-to-one examinations might be too expensive. For this reason, you can conduct an affordable test which will measure performance in a similar manner.

Experimental Validity

Experimental validity determines whether an experiment design is built correctly. Without a properly constructed study design, you won’t be able to get valid research results. With this in mind, your research design should justify such factors to be valid: 

• Have accurate results.
• Identify some relationship between variables.
• Be generalized to other situations.

Based on this, there are three main types of experimental validity:

• Internal validity When a cause-and-effect relationship is determined properly and not affected by other variables. If you can identify any causal connection between your treatment and subject’s reaction, then your experiment is internally accurate.
• External validity When research results can be applied to other similar populations. If you can employ your findings in other contexts, then your research has a high external accuracy.
• Statistical conclusion validity When your conclusion about causal relationship is correct. Any conclusion that you make should be solely based on data. Otherwise, it will be considered invalid.

If you need more information about this kind of validity, read the  internal validity vs external validity  article on our platform.

Validity: Key Takeaways

Identifying how thoroughly a student addressed different types of validity in their study is an important factor in any research critique. How well a scientist considers all factors determines whether research ‘makes sense’ and can be developed further. A high-quality study should offer evidence that proves the accuracy of chosen measurement methods. Make sure you consider each factor so you can conduct worthwhile research.

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Frequently Asked Questions About Validity

1. what is a concurrent validity design.

A concurrent validity design is a study where two measurement tests are carried out simultaneously. One of these tests is already well established, while the other one is new. Once two tests are done, researchers compare the outcomes to see if a fresh approach works.

3. What is a good discriminant validity?

To make sure that your study has a good discriminant validity, you need to prove that concepts which shouldn’t be related don’t have any connection. There is no standard score for this estimate. However, an outcome around 0.75-0.85 implies there is a discriminant accuracy.

2. How do you determine predictive validity?

To determine predictive validity you should compare the performance or behavior during the test with the subsequent behavior for which this test was developed. If you find a strong correlation and results are as expected, then your test is accurate.

4. Why is validity important in research?

It’s important to have a high research validity because it allows us to identify what questions should be included in the questionnaire. Besides, it guides researchers in the right direction. In accurate research, a chosen method will measure what is intended to be measured.

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Research validity in surveys relates to the extent at which the survey measures right elements that need to be measured. In simple terms, validity refers to how well an instrument as measures what it is intended to measure.

Reliability alone is not enough, measures need to be reliable, as well as, valid. For example, if a weight measuring scale is wrong by 4kg (it deducts 4 kg of the actual weight), it can be specified as reliable, because the scale displays the same weight every time we measure a specific item. However, the scale is not valid because it does not display the actual weight of the item.

Research validity can be divided into two groups: internal and external. It can be specified that “internal validity refers to how the research findings match reality, while external validity refers to the extend to which the research findings can be replicated to other environments” (Pelissier, 2008, p.12).

Moreover, validity can also be divided into five types:

1. Face Validity is the most basic type of validity and it is associated with a highest level of subjectivity because it is not based on any scientific approach. In other words, in this case a test may be specified as valid by a researcher because it may seem as valid, without an in-depth scientific justification.

Example: questionnaire design for a study that analyses the issues of employee performance can be assessed as valid because each individual question may seem to be addressing specific and relevant aspects of employee performance.

2. Construct Validity relates to assessment of suitability of measurement tool to measure the phenomenon being studied. Application of construct validity can be effectively facilitated with the involvement of panel of ‘experts’ closely familiar with the measure and the phenomenon.

Example: with the application of construct validity the levels of leadership competency in any given organisation can be effectively assessed by devising questionnaire to be answered by operational level employees and asking questions about the levels of their motivation to do their duties in a daily basis.

3. Criterion-Related Validity involves comparison of tests results with the outcome. This specific type of validity correlates results of assessment with another criterion of assessment.

Example: nature of customer perception of brand image of a specific company can be assessed via organising a focus group. The same issue can also be assessed through devising questionnaire to be answered by current and potential customers of the brand. The higher the level of correlation between focus group and questionnaire findings, the high the level of criterion-related validity.

4. Formative Validity refers to assessment of effectiveness of the measure in terms of providing information that can be used to improve specific aspects of the phenomenon.

Example: when developing initiatives to increase the levels of effectiveness of organisational culture if the measure is able to identify specific weaknesses of organisational culture such as employee-manager communication barriers, then the level of formative validity of the measure can be assessed as adequate.

5. Sampling Validity (similar to content validity) ensures that the area of coverage of the measure within the research area is vast. No measure is able to cover all items and elements within the phenomenon, therefore, important items and elements are selected using a specific pattern of sampling method depending on aims and objectives of the study.

Example: when assessing a leadership style exercised in a specific organisation, assessment of decision-making style would not suffice, and other issues related to leadership style such as organisational culture, personality of leaders, the nature of the industry etc. need to be taken into account as well.

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• Open access
• Published: 21 August 2024

An analysis of the psychometric properties of the medication safety competence scale in Turkish

• Ayşe Aydinli 1 &
• Kamuran Cerit 2  

BMC Nursing volume  23 , Article number:  578 ( 2024 ) Cite this article

86 Accesses

Metrics details

Considering the key roles and responsibilities of nurses in ensuring medication safety, it is necessary to understand nurses’ competence in medication safety. Therefore, it was aimed to introduce a scale evaluating the medication safety competence of nurses into Turkish and to contribute to the literature by determining the medication safety competence levels of nurses.

A methodological and descriptive research design was utilised. The population consisted of nurses in Turkey, and the sample comprised 523 nurses who volunteered to participate.

The content validity index of the scale was 0.98, and the scale showed a good fit (χ 2 /df = 3.00, RMSEA = 0.062). The Cronbach’s alpha coefficient of the scale was 0.97, indicating high reliability. The mean score was 4.12, which was considered high. Participants who were 40 years old or above, married, and graduates of health vocational schools or postgraduate programs, along with those who had received medication safety training, had higher medication safety competence scores.

This study presents strong evidence that the Turkish version of the Medication Safety Competency Scale is valid and reliable when administered to nurses. The participants in this study had high levels of medication safety competence.

Peer Review reports

Introduction

A medication error is defined as a preventable event at any stage of drug therapy that results in incorrect drug use or harms the patient [ 1 ]. According to World Health Organization (WHO) reports, medication errors account for 20% of medical errors [ 2 ]. Medication errors decrease patients’ quality of life and result in high costs for healthcare institutions [ 3 ]. They can also lead to complications, permanent disability, and death [ 1 ]. Globally, concerns about medication errors are increasing, and various reports have emphasised the importance of reducing medication errors and improving patient safety [ 2 , 4 , 5 ].

Patient safety involves ensuring that patients are not harmed while receiving care, and medication safety is among the most important elements of patient safety [ 6 ]. Medication safety can be defined as ensuring that medications have the maximum therapeutic effect while minimising and preventing adverse reactions and accidental injuries during medication use [ 4 ]. Medication safety is a multidisciplinary and multi-stage process. Nurses constitute the majority of the healthcare team and are involved in many stages of the medication administration process; they are at the centre of medication administration and are involved in the most critical stage when any potential errors reach the patient [ 7 ].

Traditional nursing curricula consider the “right principles” as a basic standard for safe medication practices. However, nurses’ role in ensuring medication safety encompasses many other principles [ 3 ]. Limiting nurses’ responsibilities regarding medication safety to the right principles does not address all aspects of errors [ 8 ]. Medication safety requires nurses to use clinical judgment before, during, and after interventions. Nurses’ experience and knowledge are integral components of safe medication management in nursing practice [ 9 ]. Adverse effects caused by improper prescription, administration, or monitoring of medications can be decreased through good nursing practice [ 8 ].

Considering the high prevalence of medication errors and the key role of nurses in ensuring medication safety, the medication safety competence of nurses must be determined [ 4 ]. However, very few studies have assessed nurses’ medication safety competence [ 3 , 4 , 5 , 6 ]. Moreover, a Turkish scale to assess nurses’ the of and attitudes towards medication safety competence is needed.

Therefore, this study introduced a Turkish version of a scale evaluating the medication safety competence of nurses and administered it to nurses, contributing to the literature by determining the medication safety competence levels of nurses.

This study adapted the Medication Safety Competence Scale (MSCS) developed by Park and Seomun (2021) for use in a Turkish context and assessed its validity and reliability; subsequently, the scale was used to determine nurses’ the of medication safety competence [ 5 ]. Differences in the of medication safety competence between nurses with varying demographic characteristics were also investigated.

Study design and participants

This study was conducted methodologically and descriptively. This study was structured and reported according to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist [ 10 ].

The population of the study comprised nurses working at various hospitals in Turkey. Convenience sampling method was used. The sample consisted of 523 nurses who were native Turkish speakers and voluntarily participated in the study. No one refused to participate in the study. There were no missing data except for 36 data related only to the worked clinic was conducted. However, these 36 data were not excluded; instead, analyses related to the clinic variable were conducted using 487 data (Tables  1 and 2 ).

The sample size for conducting CFA was determined to be at least 10 times the number of scale items [ 11 ]. This rule was met for 36 items with 523 cases. In descriptive analyses, the sufficiency of the sample size was determined by a post hoc power analysis. As a result of the post hoc power analysis conducted with G*Power 3.1.9.7, the power of the study was calculated as 85% with an effect size of 0.26 and a significance level of 0.05 [ 12 ].

Data collection

A demographic information form was used to determine the demographic characteristics of the nurses, and the MSCS was used to determine their the of medication safety competence.

The demographic information form consists of 10 questions. The form, which was prepared by the researchers in line with the literature, consists of questions with demographic characteristics of the nurses such as age, gender and worked clinics [ 3 , 4 , 5 , 6 ].

The MSCS was developed by Park and Seomun in 2021 [ 5 ]. The scale consists of 36 items divided into six subdimensions. The dimensions are patient-centred medication management (Items 1,4,5, 6,7,8, 13,24,26); multidisciplinary collaboration (Items 20,27,30,33); safety risk management (Items 2,15,16,21,25,28); management of effecting factors (Items 3,9,11,12,14,18); improvement of safety problems (Items 10,19,22,29,31,32,34,35); and responsibility in the nursing profession (Items 17,23,36). The scale is a five-point Likert scale. The total score ranges from 36 to 180. Scores between 36 and 75 represent poor medication safety competence, scores between 76 and 130 indicate moderate medication safety competence, and a score of 180 represents high medication safety competence [ 6 ].

Data were collected through an online survey between February 1 and March 31, 2023. Participants were reached via social media (WhatsApp, Instagram story, etc.) and invited to participate in the online survey prepared through Google Forms. An informed consent form was attached to the first part of the questionnaire and participation was voluntary. The response time of the questionnaires was 8–10 min.

This study was conducted in two stages: methodological and descriptive.

Methodological stage

This stage consisted of translation and psychometric testing. To adapt the scale for use in a Turkish context, permission was obtained from the researchers who developed the original scale. Back translation was used for language validity. The content validity of the Turkish version of the scale was tested. After receiving expert opinions, the scale was translated back into English (supplementary file- 1 ). After the translated version was sent to the researchers who developed the scale and approval was obtained, data collection was started with the Turkish form. Data were then collected for psychometric testing. The validity and reliability of the original scale were tested with data from the 523 participants.

Descriptive stage

In this stage, nurses’ the of medication safety competence were determined and analysed according to several demographic characteristics. Data from the 523 participants were used in this stage.

Data Analysis

Data were analysed with the Statistical Package for the Social Sciences (SPSS) v. 26.0, LISREL v. 8.80, and Microsoft Excel.

Descriptive statistics

Means and standard deviations were calculated for continuous data, and percentages were calculated for categorical data. The adequacy of the multivariate normal distribution of the data was assessed using Mardia’s skewness and kurtosis tests. Mann–Whitney U and Kruskal–Wallis tests were used for comparison between groups of nurses with different demographic characteristics.

Item analysis

To determine whether the scale had an ideal discrimination ability, the total score of the scale was ranked from high to low, and the difference between the first 27% and the last 27% was analysed. Furthermore, item-total score correlation coefficients were calculated.

Validity analysis

Eleven experts rated the items of the adapted scale from 1 to 4 for content validity (1: not appropriate; 2: partially appropriate, the item needs to be revised; 3: appropriate but minor changes are needed; 4 very appropriate). The item content validity index (I-CVI) and the scale content validity index (S-CVI) were calculated using the method proposed by Davis (1992) [ 13 ]. The I-CVI is the ratio of the number of experts who assign each item 3 or 4 points to the total number of experts. The S-CVI is the average I-CVI for all items.

Confirmatory factor analysis (CFA) was applied for the construct validity, and fit indices were evaluated. The values of chi-square (χ 2 )/degree of freedom (df), comparative fit index (CFI), root-mean-square error of approximation (RMSEA), non-normed fit index (NNFI), normed fit index (NFI), standardised root mean square residual (SRMR), root mean square residual (RMR), goodness of fit index (GFI), and adjusted goodness of fit index (AGFI) were examined. Also average variance extracted (AVE) and Construct reliability (CR) were examined for convergent validity.

Reliability analysis

Cronbach’s alpha (α) and split-half reliability were calculated to assess internal consistency. To assess test–retest reliability, intra-class correlation (ICC) was calculated by collecting data from 30 nurses at 2-week intervals. The data obtained for the test-retest were not included in the sample.

Ethical considerations

Ethical approval.

of the study was obtained from the Suleyman Demirel University Institutional Ethics Committee (decision number: 87432956-050.99-423263). Informed consent was obtained from the participating nurses in the first part of the online survey. The study was carried out in accordance with the principles of the Declaration of Helsinki.

Most participants (94.5%) were female, 61.2% were between the ages of 20–29 years, 53.7% were married, 72.7% had a Bachelor’s degree, and 54.9% had 1–5 years of professional experience. Furthermore, 79.2% of the participants reported that they had received training or courses on medication safety, 79.5% reported that medication administration principles were followed in the clinic where they worked, and 76.7% stated that medication administration was performed following the hospital’s medication administration rules and procedures (Table  1 ). The results of the methodological and descriptive stages of the study are provided in the following two sections.

Results of the methodological stage

The scale was translated into Turkish by four translators who were native Turkish speakers and fluent in English. The researchers then combined the four translations into a single form. In the second step, this form was translated back into English by an expert who was not one of the previous translators.

The expert review was conducted by nine nursing instructors and two nurses with master’s degrees. The I-CVI ranged from 0.80 to 1.00, and the S-CVI was 0.98. To assess the face validity of the Turkish form, a preliminary application was performed with 20 nurses. To ensure that they were comprehensible in Turkish, the expression “human factors” in the item “Understanding the role of human factors, such as fatigue, that affect medication safety” was changed to “personal factors”, and the expression “understanding the role” in “Understanding the role of environmental factors such as workflow, ergonomics, and resources, which affect medication safety” was changed to “understanding the effect”. With these adjustments, the scale form adapted to Turkish was finalised. Data from the pilot study were not included in the sample.

The results of CFA are shown in Table  3 . The fitness indices of the original scale (model 1) (χ 2 /df = 1921.97/579 = 3.32, RMSEA = 0.067, CFI = 0.98) were determined to be at an acceptable level (Fig.  1 ). However, modification indices were examined, and the original scale was modified sequentially as follows: item 28 and item 31, item 25 and item 31, item 10 and item 11, respectively. The modification of model 2 was achieved by freeing the error terms (permitting correlated errors) of the items without excluding any items (Fig.  2 ).

CFA results of Model I: standard loadings and error variances

CFA results of Model II: standard loadings and error variances

The fitness indices of the final scale (model 2) were as follows: χ 2 /df = 3.00, RMSEA = 0.062, CFI = 0.99, NFI = 0.98, GFI = 0.78, AGFI = 0.74. The standard loadings of the items in the final scale ranged from 0.46 to 0.83. The squared multiple correlations (SMC-R 2 ) ranged from 0.21 to 0.68 (Table  4 ). Furthermore, the fit indices attained in both models were acceptable. Nevertheless, model 2 exhibited superior χ2/df and RMSEA values, indicating that it outperformed model 1.

For convergent validity, the CRs ranged from 0.68 to 0.79 and were higher than the AVE values (0.40 to 0.60). The correlation between factors ranged from 0.687 to 0.868 (Table  5 ). Item analyses revealed that the item-total correlations were between 0.46 and 0.77 (Table  4 ). A statistically significant difference (t: −30.601, p  < 0.001) was observed between the mean scores of the groups with the lowest 27% of scores and the highest 27% of scores.

As shown in Table  5 , the Cronbach’s alpha coefficient of the scale was 0.97. The split-half reliability was 0.912, and the test–retest reliability (ICC) was 0.939.

Result of the descriptive stage

The participants’ total mean score of MSCS was 147.81 ± 21.29, indicating a average level of medication safety competence. The lowest score was obtained in the RNP dimension (11.88 ± 2.05 points) and the highest score was obtained in the PCMM dimension (38.21 ± 5.14 points) (Table  5 ). Table  2 presents the comparison of participants’ the of medication safety competence according to their demographic characteristics.

No differences were observed in the participants’ the of medication safety competence according to gender and the type of clinic they worked in. However, significant differences were found according to age, marital status, educational level, professional experience, and the type of hospital in which the nurses worked. Participants aged 40 years and older had higher the of medication safety competence than those aged 20–29 years; married participants had higher than single participants; and those with health vocational school and postgraduate degrees had higher than those with undergraduate degrees. In addition, nurses who received training or courses on medication safety, those who thought that the principles of medication administration were followed in the clinic where they worked, and those who thought that medication administration was performed following the hospital’s medication administration rules and procedures had higher the of medication safety competence ( p  < 0.05).

Discussion of the Methodological Stage

This study was conducted to determine the validity and reliability of a Turkish version of the MSCS. This study found that the Turkish version of the MSC scale meets the criteria of language validity, content validity, construct validity, and reliability. The validity and reliability of the scale have also been confirmed in Chinese and Persian [ 4 , 6 ].

In this study, the Turkish version of the scale was created using the back translation method for language validity. Then the content validity of the scale was evaluated according to Davis’s (1992) technique [ 13 ]. Because the CVI values of all items were above 0.80, no items were removed at this stage. CFA was performed to verify construct validity. Two models were analysed: the original scale (model 1) and the final scale (model 2). According to the factor loadings and modification indices of model 1 and model 2, the measurement validity of the Turkish version of the MSCS was confirmed. Thus, no items were removed from the scale at this stage.

The χ 2 /df of the final scale was 3.00; this value is considered acceptable, as it is less than 5 [ 14 ]. The RMSEA value of 0.062 is an important indicator of the acceptable fit of the final scale. The RMR (0.042) and SRMR (0.057) values showed perfect and acceptable fit, respectively [ 15 ]. The CFI was above 0.95, indicating perfect fit [ 16 ]. The NFI and NNFI were also above 0.95, indicating perfect fit [ 17 , 18 ]. In CFA, it is recommended that the factor loadings of the items factors should be above 0.50 [ 10 ]. In this study, the factor loadings of the items were between 0.46 and 0.83 (Table  4 ). Thus, the fit indices and item factor loadings confirmed the construct validity of the final scale. The AVEs of the factors were higher than 0.50 in all subdimensions except for RNP and MEF. Moreover, the CRs were between 0.68 and 0.79, higher than the recommended value of 0.70 and the AVE values (Table  5 ). These results confirmed the scale’s convergent validity.

Finally, item analyses showed that the items in the scale had good discrimination. Similarly, the Chinese version [ 4 ], the original scale [ 5 ] and the Persian version [ 6 ] also reported high factor loadings, acceptable fit indices and CRs above 0.70. In line with these results, it can be said that the scale provides an adequate level of validity.

Reliability

The reliability of the scale was evaluated by calculating split-half reliability, Cronbach’s alpha, CR and ICC. Cronbach’s alpha value should be at least 0.70 [ 11 , 19 ]. In the present study, Cronbach’s alpha was 0.97, whereas it was 0.94 in the Chinese version [ 4 ], 0.94 in the original scale [ 5 ], and 0.96 in the Persian version [ 6 ]. The CR values reported as more appropriate reliability measures for CFA-based studies [ 11 ] are higher than the proposed value of 0.70. These results show that the scale has good internal reliability in samples from different cultures. In this study, the split-half reliability of the scale was 0.912, and the test–retest reliability was 0.939. The split-half reliability of the Chinese version of the scale was 0.671, and the test–retest reliability was 0.703 [ 4 ]. For the Persian version, the test–retest reliability was also 0.90 [ 6 ]. These values indicate the stability of the various versions of the scale [ 20 ].

Discussion of the descriptive phase

To date, nurses’ medication safety competency has generally been examined within the framework of the right principles [ 21 , 22 ] or reporting medication errors [ 23 , 24 ]. However, medication safety is a concept that transcends the right principles [ 3 , 8 ], and previous studies measuring nurses’ medication safety competency have been insufficient. The medication safety competence scores of the nurses in this study were average. Mohebi et al. (2024) also reported the medication safety competence of nursing students at an average level. These results could indicate that the medication safety competence of nurses and students were adequate but needed further development [ 25 ]. These results may be explained by the fact that most of the nurses in the study received training or courses on medication safety. Moreover, in recent years, in Turkey and other countries, patient and medication safety issues have been important issues of health policies and hospitals in Turkey. Although the issue is important, a study conducted in Turkey found that almost half of the nurses reported that no institutional procedures were in place for medication safety in hospitals [ 26 ].

In this study nurses’ the of medication safety competence differ according to age, marital status, education level, professional experience, hospital type, and the types of training or courses on medication safety the nurses have received. More studies investigating nurses’ the of medication safety competence are warranted, along with studies assessing differences in the of medication safety competence between nurses with varying demographic characteristics. This study provides strong evidence for the reliability and validity of the scale in Turkish. It is also the first study to determine the medication safety competencies of nurses working in Turkey.

This study presents strong evidence that the Turkish version of the MSCS is valid and reliable among nurses. The medication safety competency levels of the nurses participating in this study were average. The assessment results of the scale provide a reference for nursing administrators to help them formulate educational plans improve the medication safety competence of nurses.

Limitations

One of the strengths of this study is its application of the scale to a large sample. Although the methodological results are important, the adapted scale is specific to nurses in Turkey. However, the results regarding nurses’ medication safety competence obtained in the study’s second stage provide a substantial contribution to the literature.

Data availability

Data Availability StatementThe data supporting this study’s findings are available on re-quest from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

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We thank the nurses who participated in this study and the experts who suggested content validity.

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Ayşe Aydinli

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AA and KC contributed to the study aim, research design, and overall structure of the manuscript. AA collected the data. KC conducted all the statistical analyses and drafted the manuscript. All authors have read and agreed to the published version of the manuscript.

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Aydinli, A., Cerit, K. An analysis of the psychometric properties of the medication safety competence scale in Turkish. BMC Nurs 23 , 578 (2024). https://doi.org/10.1186/s12912-024-02240-0

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